Learning communities supported by autonomic recommendation mechanism

Abstract

Peer-to-peer (P2P) offers good solutions for many applications such as large data sharing and collaboration. Thus, it appears as a powerful paradigm to develop scalable distributed applications, as reflected by the increasing number of emerging projects based on this technology. However, building trustworthy P2P collaborative tool is difficult because they must be deployed on a large number of autonomous nodes, which may be part of the virtual community and to make the collaboration effectively happen among the nodes. Within this scenario, this article presents an autonomic recommendation mechanism of knowledge chains, which is based on the apprentice profile and his current knowledge to recommend the best learning strategy after the analysis of the learning community in this peer-to-peer environment.